A Study on Continuous Wavelet Transform for Fault Detection in Electric Motors

Ayaz, Emine; Öztürk, A.; Şeker, Serhat

doi:10.1109/siu.2006.1659713

Cited by 1 publication

(1 citation statement)

References 4 publications

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“…In this section, we present a list of works on the rotary machinery prognostics as well as other critical components that are widely used in engineering platforms, engines and mechanical systems. Table 5 gives an introductory summary of tools for common critical components, regarding the components' issue and possible failure modes, characteristics, common available data types, common features and algorithms ap- In addition, there are papers that discuss the prognostics on various components/system including batteries [216,217,218,219,220,221,222,223] (prognostics for batteries appears to be at a more advanced stage than prognostics for structures [3]), DC-motor [215,224,225], electric motors [226,227,228] boiler [124,125],…”

Section: Applicationsmentioning

confidence: 99%

Survey of prognostics methods for condition-based maintenance in engineering systems

Taheri,

Kolmanovsky,

Gusikhin

2019

Preprint

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It is not surprising that the idea of efficient maintenance algorithms (originally motivated by strict emission regulations, and now driven by safety issues, logistics and customer satisfaction) has culminated in the socalled condition-based maintenance program. Condition-based program/monitoring consists of two major tasks, i.e., diagnostics and prognostics each of which has provided the impetus and technical challenges to the scientists and engineers in various fields of engineering. Prognostics deals with the prediction of the remaining useful life, future condition, or probability of reliable operation of an equipment based on the acquired condition monitoring data. This approach to modern maintenance practice promises to reduce the downtime, spares inventory, maintenance costs, and safety hazards. Given the significance of prognostics capabilities and the maturity of condition monitoring technology, there have been an increasing number of publications on machinery prognostics in the past few years. These publications cover a wide range of issues important to prognostics. Fortunately, improvement in computational resources technology has come to the aid of engineers by presenting more powerful onboard computational resources to make some aspects of these new problems tractable. In addition, it is possible to even leverage connected vehicle information through cloud-computing. Our goal is to provide a report on the state of the art and to summarize some of the recent advances in prognostics with the emphasis on models, algorithms and technologies used for data processing and decision making.

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